1. 366
6.5

2. Sampling Defined / The idea Making inference about a larger population
What is the population
Some particular value in the population
estimating a parameter

3. Sampling

4. Sampling Population must be defined If interested in opinions of...
All adults
Registered voters
Likely voters
Actual voters
These are all distinct populations

5. Sampling Population must be defined If interested in opinions of...
People in Whatcom County
Voters in Whatcom County
People in Bellingham
Voters in Bellingham
Likely voters in Bellingham
These are all distinct populations

6. Sampling Population must be defined If interested in opinions of...
Students at WWU
Seniors at WWU (xxx # of credits & up)
Students in College of Arts & Sciences
etc.
These are all distinct populations; who should be included, excluded

7. Sampling Sampling unit A single member of the population
a case
if population = voters
sampling unit = registered voter (?)
If population = conflicts / wars
sampling unit = nation of a certain size; conflict of particular duration

8. Sampling Sampling Frame
Once clear about what population & units are, how do we find them?
Frame = complete list of population
Registered voters; Students at WWU
In reality this may not exist
e.g., all people living in the US

9. Sampling Sampling Frame US Census How get ‘the list?’
$3billion; 500,000 workers...

10. Sampling Sampling Frame Registered voters; Students at WWU
Piece of cake?
Accuracy of sample depends on comprehensiveness of frame

11. Sampling Sampling Frame Ahead of time, evaluate for problems
Missing elements
New residents, newly registered voters, ?
Clusters
Census tracts, city blocks, Zip code, Area code, prefix
Take random draw of clusters, then random draw of households in cluster

12. Classic Sample Failure
1936 Literary Digest Survey
Survey of 2.4 million Americans
Predicted Alf Landon 57%, FDR 43%
Actual result FDR 62%, Landon 38%
Frame = 10 million people
subscribers to Digest; phone directories; club memberships

13. Sampling Sampling Frame Ahead of time, evaluate for problems
Blank elements
Phone directories (address w/o #)
Phone #s (unassigned prefixes; fax machine; pager)
List of all residents when population = voters

14. Classic Sample Failure
1936 Literary Digest Survey
What went wrong?

15. Classic Sample Failure
2000 & 2004 & 2012 (WI) US Exit polls
Surveys of tens of thousands
2000 initially predicted Gore win FL
Actually, Bush won
2004 initially predicted Kerry win OH
Frame:
Key precincts, people voting at polling places

16. 2004 VNS Exit Polls, Ohio

17. 2004 Exit Polls State Exit (Bush) Actual Diff FL 49 52.5 -3.5 PA 46.5
48.9
-2.4 OH
51.3
-2.3 MI
46.9
48.3
-1.3 NJ
44.9 -2 NH
49.3
-4.4 NY
36.7
41.2
-4.5

18. Exit Polls Exit poll difficulty: Identify representative precincts
Sample throughout day
Estimate non-polling place vote
'Weight' data to account for sample problems:
o group difference in non-response
o turnout differences
o vote by mail
All before 8pm
After polls close, weight again...and again

19. “This can’t happen in America. Maybe in Ohio...”

20. Classic Sample Failure
2000 & 2004 US Exit polls
What went (goes) wrong?
also response bias that favors Democrats

21. 2016 Exit polls A bit better https://www.electiontonight.com/
Many states too close to call; no networks called the election until Clinton conceeded
(AP did).

22. Sample Designs Probability vs. Non probability sampling
Probability sample
We know the probability that each unit in the population has of being in the sample
Non probability sample
We don’t know if every unit has a fixed chance of being in sample

23. Probability Sample & Normal Distribution
Normal distribution has “areas” under it
percent of observations in sample in terms of distance from mean (in s.d units)
We use this to estimate the probability an observation will occur.

24. Probability Sample & Normal Distribution
Study: Population teen girls texting
Sample: teen girls
Sample unit: number of texts per day
mean 70 texts per day, SD = 10 (z = +1.00)
What is probability of selecting a teen girl in sample who texts 70 – 80 times per day?

25. Sample Design Probability sample
If 22% of population are white males over 21 years of age...
a .22 probability that a white male over 21 would end up in sample

26. Sampling

27. Sample Design Probability sample
If study repeated w/ different samples, high likelihood that results similar
We can estimate likelihood that things observed in the sample are representative of the population

28. Sample Design Real world probability sample problems
Population = likely voters
Good sample frame?
Voters yes, likely voters no
Proper randomization
You try it
Missing elements
Land line vs. cell phones

29. Probability Samples Simple random sampling Systematic samples
Stratified samples
Cluster samples

30. Probability Samples Simple random sampling
List each unit (person) in population
Give each a number (List from 1 to n)
Use random # generator
If 1207 comes up, select #1207 from list
Repeat

31. Probability Samples Systematic sample Have list of population, 1 – nth
Find random #, start there on list
Pick each kth unit (person) on list
Hope there is no structure to list
Starting point random, increment random
Easier
Kind of how exit polls work at polling place

32. Probability Sample Stratified sample
Use available information from the population
Dived so elements w/ in groups (strata) are more alike than population
A series of homogeneous groups
Stratify by race/ethnicity; income
randomly sample in each subgroup; maybe over-sample a (smaller) group
Combine samples into one
Cheaper

33. Probability Samples Cluster sample Identify clusters (groups)
Select large groups by random
Cities, congressional districts, states, neighborhoods
Randomly sample within cluster

34. Probability Samples Simple random sampling Systematic samples
Stratified samples
Cluster samples
Other types, some of these used together

35. Non-probability Samples
Convenience sample (Biased)
All students in this class
Population = WWU students
First 200 people walking down Railroad Ave.
Population = Whatcom County voters
No way to know representativeness of sample

36. Non-probability samples
Purposive sample
Units selected subjectively
Chance of being selected depends on researcher’s judgment
“Critical elections”
Population = all US Presidential elections
“Major wars”
Population = all wars

37. Non-probability sample
Quota sample
Purposively select sample as representative as possible
Use know characteristics of population
Target quota based on know characteristics

38. Non-probability sample
Quota sample
WWU (Fake example)
57% female, 43% male
45% A&S; 25% CST; 10% CBE; 10% Huxley; 10% other
Age
Ethnicity

39. Non-probability sample
Quota sample
Whatcom Co. (Fake example)
Gender
Age
Partisanship
City resident vs. County resident
Monitor demographics of respondents as you go

40. Non-probability sample
Quota sample
Poor person’s random sampling
Can fail to predict
surveys predicted Dewey to win
None targeted partisanship

41. Internet Samples Opt-in Provide people computers
Huge samples asked to do interviews
“Weight” data after responses to represent population

42. Sample size
If sample random (ish), precision of estimates depend on size
Larger = more precise estimate, all else equal
Very large doesn’t add much precision

43. Sample size Diminishing returns on size
Depends on scale of population, subgroups
Whatcom Co.
State of WA
USA

44. Sample size Diminishing returns on size
Depends on scale of population, subgroups
Whatcom Co.
State of WA
USA